model-merging
Category: multimodal-and-emerging · License: MIT · Version: 1.0.0
Tags: Emerging Techniques, Model Merging, Mergekit, SLERP, TIES, DARE, Task Arithmetic, Model Fusion, No Retraining, Multi-Capability, Arcee AI
When to use it
Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.Example requests
Ask your agent in plain language, for example:- “How do I merge two Mistral-7B fine-tunes with SLERP?”
- “I have three Llama-2-7B fine-tunes on my disk: ./models/math-ft, ./models/code-ft and ./models/chat-ft, all from the same base meta-llama/Llama-2-7b-hf. I tried a linear merge and the output is garbled and worse than any single model. Can you write me a mergekit config that uses TIES with density 0.5 and weights, and explain how to run it on CPU?”
- “I have a few open-weight models, each fine-tuned for a different skill. I have no GPU budget for more training. Can I produce one model that is decent at all of those skills by combining their weights, and how would I pick the mixing coefficients without labeled data?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole multimodal-and-emerging category with /plugin install multimodal-and-emerging@research-agent-skills.